Testing the structure of human cognitive ability using evidence obtained from the impact of brain lesions over abilities

Protzko, John; Colom, Roberto

doi:10.1016/j.intell.2021.101581

Cited by 13 publications

(11 citation statements)

References 114 publications

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“…Finally, the low to moderate correlations ( r = −.24 to .43) between the corresponding centrality metrics (from both models) with traditional psychometric g -loadings indicates that PNA metrics provide valuable information regarding the relations of intelligence measures not captured by a psychometric g -factor. This finding is consistent with the fact that PNA is conceptually akin to common cause bifactor models that remove the large psychometric g variance before exploring secondary residual variance-based common factors ( Protzko and Colom 2021b ). As per Jensen ’s ( 1998 ) principle of adequate psychometric sampling, the linear combination of the 18 remaining diverse measures serves as a proxy of psychometric g .…”

Section: Resultssupporting

confidence: 84%

“… Fried ( 2020 ) and others ( Eronen and Bringmann 2021 ; Kovacs and Conway 2019 ; Protzko and Colom 2021a , 2021b ) have cogently explained why the cavalier use of g -like terms (e.g., g for general intelligence; p for general psychopathology) and the failure to differentiate between theoretical and psychometric models, contributes to the theory crises in psychology. This is particularly prevalent in psychological fields heavily dependent on structural analysis methods, such as IQ battery structural research.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Modern network-based models of intelligence (e.g., process overlap theory; dynamic mutualism) ( Protzko and Colom 2021a , 2021b ; van der Maas et al 2019 ) eschew the assumption that the positive manifold among cognitive ability tests is due to latent unobserved common causes. Instead, network models (and psychometric network analysis methods, PNA) are based on the assumption that the positive definite nature of cognitive ability test correlations is the result of the non-linear interaction of multiple biological and psychological factors, sans the invocation of underlying latent common cause explanatory traits or factors ( Hampshire et al 2012 ; van der Maas et al 2019 ).…”

Section: Literature Reviewmentioning

confidence: 99%

“…It is hard to ignore the robust replication of the existence of psychometric g as well as its documented dominance as the single best individual difference characteristic predictor of a wide variety of outcomes ( Deary 2012 ; Lubinski 2004 ). The theoretical origin of the g factor derives from the consistent finding that cognitive ability tests covary positively ( Protzko and Colom 2021a , 2021b ). This empirical regularity is the positive manifold ( Jensen 1998 ).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Psychometric Network Analysis of CHC Intelligence Measures: Implications for Research, Theory, and Interpretation of Broad CHC Scores “Beyond g”

McGrew¹,

Schneider

Decker

et al. 2023

J. Intell.

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For over a century, the structure of intelligence has been dominated by factor analytic methods that presume tests are indicators of latent entities (e.g., general intelligence or g). Recently, psychometric network methods and theories (e.g., process overlap theory; dynamic mutualism) have provided alternatives to g-centric factor models. However, few studies have investigated contemporary cognitive measures using network methods. We apply a Gaussian graphical network model to the age 9–19 standardization sample of the Woodcock–Johnson Tests of Cognitive Ability—Fourth Edition. Results support the primary broad abilities from the Cattell–Horn–Carroll (CHC) theory and suggest that the working memory–attentional control complex may be central to understanding a CHC network model of intelligence. Supplementary multidimensional scaling analyses indicate the existence of possible higher-order dimensions (PPIK; triadic theory; System I-II cognitive processing) as well as separate learning and retrieval aspects of long-term memory. Overall, the network approach offers a viable alternative to factor models with a g-centric bias (i.e., bifactor models) that have led to erroneous conclusions regarding the utility of broad CHC scores in test interpretation beyond the full-scale IQ, g.

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Section: Resultssupporting

confidence: 84%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Psychometric Network Analysis of CHC Intelligence Measures: Implications for Research, Theory, and Interpretation of Broad CHC Scores “Beyond g”

McGrew¹,

Schneider

Decker

et al. 2023

J. Intell.

View full text Add to dashboard Cite

show abstract

“…Researchers using contemporary cognitive theories of intelligence (e.g., dynamic mutualism; process overlap theory; wired intelligence) have shown valid alternative non-latent trait common cause factor explanations of the positive manifold of intelligence tests. Furthermore, the previously mentioned period of 100+ years of research regarding general intelligence has also robustly demonstrated that there is yet no known biological or cognitive process theoretical basis of psychometric g ( Barbey 2018 ; Detterman et al 2016 ; Kovacs and Conway 2019 ; van der Maas et al 2019 ; Protzko and Colom 2021 ). Therefore, in this paper, the GIA score is interpreted to reflect an emergent property that is a pragmatic statistical proxy for psychometric g , and not a theoretical individual differences latent trait characteristic of people.…”

Section: Resultsmentioning

confidence: 99%

Linguistic Influences on Cognitive Test Performance: Examinee Characteristics Are More Important than Test Characteristics

et al. 2022

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Consideration of the influence of English language skills during testing is an understandable requirement for fair and valid cognitive test interpretation. Several professional standards and expert recommendations exist to guide psychologists as they attempt to engage in best practices when assessing English learners (ELs). Nonetheless, relatively few evidence-based recommendations for practice have been specified for psychologists. To address this issue, we used a mixed-effects modeling approach to examine the influences of test characteristics (i.e., test directions) and examinee characteristics (i.e., expressive and receptive language abilities) on cognitive test performance. Our results suggest that language abilities appear to have a significant influence on cognitive test performance, whereas test characteristics do not influence performance, after accounting for language abilities. Implications for practice include the assessment of expressive and receptive language abilities of EL students prior to administering, scoring, and interpreting cognitive test scores.

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Investigating cognitive neuroscience theories of human intelligence: A connectome‐based predictive modeling approach

Anderson

Barbey

2022

Human Brain Mapping

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Central to modern neuroscientific theories of human intelligence is the notion that general intelligence depends on a primary brain region or network, engaging spatially localized (rather than global) neural representations. Recent findings in network neuroscience, however, challenge this assumption, providing evidence that general intelligence may depend on system‐wide network mechanisms, suggesting that local representations are necessary but not sufficient to account for the neural architecture of human intelligence. Despite the importance of this key theoretical distinction, prior research has not systematically investigated the role of local versus global neural representations in predicting general intelligence. We conducted a large‐scale connectome‐based predictive modeling study ( N = 297), administering resting‐state fMRI and a comprehensive cognitive battery to evaluate the efficacy of modern neuroscientific theories of human intelligence, including spatially localized theories (Lateral Prefrontal Cortex Theory, Parieto‐Frontal Integration Theory, and Multiple Demand Theory) and recent global accounts (Process Overlap Theory and Network Neuroscience Theory). The results of our study demonstrate that general intelligence can be predicted by local functional connectivity profiles but is most robustly explained by global profiles of whole‐brain connectivity. Our findings further suggest that the improved efficacy of global theories is not reducible to a greater strength or number of connections, but instead results from considering both strong and weak connections that provide the basis for intelligence (as predicted by the Network Neuroscience Theory). Our results highlight the importance of considering local neural representations in the context of a global information‐processing architecture, suggesting future directions for theory‐driven research on system‐wide network mechanisms underlying general intelligence.

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Testing the structure of human cognitive ability using evidence obtained from the impact of brain lesions over abilities

Cited by 13 publications

References 114 publications

A Psychometric Network Analysis of CHC Intelligence Measures: Implications for Research, Theory, and Interpretation of Broad CHC Scores “Beyond g”

A Psychometric Network Analysis of CHC Intelligence Measures: Implications for Research, Theory, and Interpretation of Broad CHC Scores “Beyond g”

Linguistic Influences on Cognitive Test Performance: Examinee Characteristics Are More Important than Test Characteristics

Investigating cognitive neuroscience theories of human intelligence: A connectome‐based predictive modeling approach

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